Adaptive optimization algorithm for fuzzy control of fuel cell range extender vehicle*

Fuzzy control strategy is a common approach for vehicle control of fuel cell hybrid power systems. Fuzzy control parameters make it difficult to handle multiple objective optimization problems, and the influence of subjective experience is large. In order to solve the above problems, we propose an adaptive chaotic particle swarm optimization (ACPSO) algorithm based on PSO and chaotic sequences which aims to achieve the combined optimal of fuel cell durability and vehicle economy. We complete the optimization of the fuzzy control parameters, reducing the possibility of getting stuck in the local optimal case, and this algorithm can search for the global optimal value more quickly. By comparing with the genetic algorithm, the standard particle swarm optimization algorithm, and the adaptive particle swarm optimization algorithm, we show that the economy of the adaptive chaotic particle swarm optimization algorithm is 7.5 percent higher, which validates the superiority and effectiveness of the proposed algorithm.